Data driven development of haptic models for needle biopsy phantoms

Needle biopsy is an important and common procedure for lesion detection or tissue extraction within the human body. Physicians conducting such procedures rely primarily on the sense of touch (kinesthetic feedback from needle) to estimate the current needle position and organs within its vicinity. This skill takes time to acquire and mature, often by biopsies on live patients. Medical residents and fellow trainees thus have limited opportunities both in terms of real life scenarios as well as testing platforms to develop and validate their skills. This paper focuses on building a biopsy simulator for training on virtual phantoms (using both visual and force feedback) and cross validation using a real physical phantom. In order to develop a virtual-haptic model of biopsy phantom, material testing experiments were conducted to obtain motion-force profiles from an instrumented 6-DOF robot platform serving as a needle driver. The measured force-displacement data was then used to develop three types of haptic models for the phantom to calculate the force feedback for the haptic device. Neural network based models provided a more accurate force-reflection model compared to the other two methods from the literature and will form the basis of the virtual phantoms within our framework.